This disclosure relates to the generation and use of image digests to locate images with matching image content.
Due to the popularity of digital technology, more and more digital images are being created and stored every day. The increasing volume of digital images being produced introduces problems for managing image repositories. Often, a user cannot determine if an image already exists in an image repository without exhaustively searching through all the existing images stored in the image repository.
Further complications may arise from the fact that two images that appear identical to the human eye when printed via an output device, such as a display or printer, may have different digital representations when stored electronically on digital storage media. For example, an original digital image stored in a first image file format may be digitally significantly different from, for example, a version of the original digital image file that has been saved in another file format, a version of the original digital image file that has been digitally compressed, a version of the original digital image file that has been digitally enhanced using pixel processing/manipulation operations, a version of the original digital image file that has been manipulated using distinct transforms, a version of the original digital image file with either a higher pixel resolution or a lower pixel resolution than the original digital image; a digital file generated by scanning a printed hardcopy of the original digital image; etc.
Further, some copies of an original image may have been cropped, compressed, resized and/or enhanced. Other copies may have been rotated, or may have been generated with modified control parameters, such as a higher or lower contrast ratio setting than were used to generate the original image. Further, content items within the images may have been manually or electronically edited to add, remove or change small features within the image.
Generally, stored images may go through several distortions and these distorted versions may be either archived in image repositories or made available as query images for use in locating an original or otherwise related image. Recent research in image hashes/digests has addressed this problem to some extent. An image digest is simply a function of the image content that evaluates to a vector that is relatively short, as compared with the image size. For example, see M. Schneider and S. F. Chang, “A robust content based digital signature for image authentication,” Proc. IEEE Conf. on Image Processing, vol. 3, pp. 227-230, September 1996; R. Venkatesan, S. M. Koon, M. H. Jakubowski, and P. Moulin, “Robust Image Hashing,” Proc. IEEE Conf. on Image Processing, pp. 664-666, September 2000; and V. Monga and B. L. Evans, “Robust Perceptual Image Hashing Using Feature Points,” Proc. IEEE Conf. on Image Processing, 2004.
Previous research has focused on the creation of image digests that are robust under common signal and image processing operations, while geometric distortions related to a wide range of remaining distortions have not been addressed. For example, creating an image digest using traditional cryptographic or repository hashes poses a problem in that such image digests are sensitive to very small changes to the image data. As a result, image digests generated from perceptually similar images using such techniques may not be sufficiently similar for use in identifying the images as perceptually similar.
Further, such techniques suffer from significant technical difficulties in capturing image features that are robust under particular distortions. More often than not the selection of robust features using such techniques is ad-hoc and on many occasion lacks generality e.g. it may be dependent on whether the image is outdoor vs. indoor etc. This limits both the development as well as application of such techniques.